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Excel for Data Analysis Power Query, Power Pivot and DAX

   Author: Baturi   |   26 January 2026   |   Comments icon: 0


Free Download Excel for Data Analysis Power Query, Power Pivot and DAX
Published 1/2026
Created by Excelsirji - Data Analytics Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 99 Lectures ( 8h 14m ) | Size: 9.51 GB


Data Transformation, Data Modeling, and Analysis Using Power Query, Power Pivot, and DAX in Excel
What you'll learn
✓ Build a complete Excel data analysis workflow using Power Query, Power Pivot, and DAX
✓ Import, clean, transform, and combine data from multiple sources using Power Query
✓ Automate repetitive data preparation steps to save time and reduce manual errors
✓ Create scalable data models and relationships using Power Pivot best practices
✓ Write DAX measures for advanced calculations, filtering, and time intelligence
✓ Apply real world scenarios to build accurate and reliable Excel reports
Requirements
● A Windows PC with Microsoft Excel (2016 or later) installed
Description
Excel data analysis is not just about charts or PivotTables. In real projects, most of the time is spent cleaning data, combining files, fixing structures, and creating reliable calculations. This course helps you build a complete Excel data analysis workflow using Power Query, Power Pivot, and DAX.
You will first understand how data flows in Excel and when to use each tool. This foundation makes everything else easier and more practical.
In this course, you will learn how to
• Build an end to end Excel data analysis workflow
• Decide when to use Power Query, Power Pivot, and DAX
• Work with real world datasets instead of sample files
The course then dives deep into Power Query. You will learn how to connect to different data sources and use the Query Editor to clean and transform data efficiently.
Power Query topics covered include
• Import data from Excel files, folders, text files, and webpages
• Clean and transform text, number, and date columns
• Create index, conditional, and custom columns
• Group, summarize, pivot, and unpivot data
• Merge and append queries to combine datasets
• Automate repetitive data preparation tasks
You will practice these skills using healthcare and sales datasets to reinforce what you learn.
After data transformation, the course moves into data modeling with Power Pivot.
You will learn data modeling concepts such as
• Fact and dimension tables
• Primary and foreign keys
• Building and managing table relationships
• Active vs inactive relationships
• Filter direction and best practices
Using real datasets like AdventureWorks and university student data, you will build clean and scalable data models.
Finally, the course focuses on DAX, the formula language used for advanced analysis in Excel.
DAX topics covered include
• Calculated columns vs measures
• Understanding filter context
• Writing basic and advanced DAX formulas
• CALCULATE, FILTER, ALL, RELATED, and iterator functions
• Time intelligence functions for date based analysis
By the end of this course, you will be able to clean raw data, build reliable data models, and create powerful calculations for real world reporting inside Excel.
Who this course is for
• Students and beginners who want to learn Excel for real world data analysis.
• Working professionals who handle large datasets and recurring reports in Excel.
• Analysts, finance, operations, and business users who want faster and more accurate reporting.
• Anyone looking to learn Power Query, Power Pivot, and DAX from scratch.
Let's get started.
Who this course is for
■ Students and beginners who want to learn Excel for real world data analysis
■ Working professionals who handle large datasets and recurring reports in Excel
■ Analysts, finance, operations, and business users who want faster and more accurate reporting
■ Anyone looking to learn Power Query, Power Pivot, and DAX from scratch
Homepage
https://www.udemy.com/course/excel-for-data-analysis-power-query-power-pivot-and-dax/


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